Computational Intelligence Applications for Text and Sentiment Data Analysis explores the most recent advances in text information processing and data analysis technologies, specifically focusing on sentiment analysis from multifaceted data. The book investigates a wide range of challenges involved in the accurate analysis of online sentiments, including how to i) identify subjective information from text, i.e., exclusion of ‘neutral’ or ‘factual’ comments that do not carry sentiment information, ii) identify sentiment polarity, and iii) domain dependency. Spam and fake news detection, short abbreviation, sarcasm, word negation, and a lot of word ambiguity are also explored. Further chapters look at the difficult process of extracting sentiment from different multimodal information (audio, video and text), semantic concepts. In each chapter, the book's authors explore how computational intelligence (CI) techniques, such as deep learning, convolutional neural network, fuzzy and rough set, global optimizers, and hybrid machine learning techniques play an important role in solving the inherent problems of sentiment analysis applications.

Emotion Analysis from Natural Language Texts is gaining ground under the umbrella of subjectivity analysis and affect computing. In this Book, concepts, motivation, approaches and applications of Emotion Analysis are discussed in terms of the main challenging tasks such as feature representation schema and emotion classification at word and sentence level granularities. A prototype is described and assessed to analyse emotions, its components and their association from different available datasets. Experiments and final outcomes highlight the promise of the approach and some open research problems.